class_docstr: :param str method: It supports 'minmax', 'max' and 'standard'. For 'minmax', data will be 
      substracted by min value in data and dividing by range of max value and min value. For
      'max', data will be divided by max value only. For 'standard', data will be substracted
      by mean value and dividing by value of standard deviation. If 'random' is used, method 
      will be picked randomly in each augment.
  :param tuple zone: Assign a zone for augmentation. Default value is (0.2, 0.8) which means that no any
      augmentation will be applied in first 20% and last 20% of whole audio.
  :param float coverage: Portion of augmentation. Value should be between 0 and 1. If `0.1` is assigned, augment
      operation will be applied to target audio segment. For example, the audio duration is 60 seconds while
      zone and coverage are (0.2, 0.8) and 0.7 respectively. 25.2 seconds ((0.8-0.2)*0.7*60) audio will be
      augmented.
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.audio as naa
  >>> aug = naa.NormalizeAug()
code: def substitute(self, data):
      start_pos, end_pos = self.get_augment_range_by_coverage(data)
  
      method = (
          self.random_method()
          if self.method == "random"
          else self.method
      )
  
      if not self.stateless:
          self.start_pos = start_pos
          self.end_pos = end_pos
          self.run_method = method
  
      return self.model.manipulate(
          data, method=method, start_pos=start_pos, end_pos=end_pos
      )
  
file_path: nlpaug/nlpaug/augmenter/audio/normalization.py
imports: import random
  from nlpaug.augmenter.audio import AudioAugmenter
  import nlpaug.model.audio
  from nlpaug.util import Action, WarningMessage
label_desc: Use image node_id 3 for calling the NormalizeAug obj's underlying member method code with example usage: obj.substitute(data) and returns: self